WaterHE-NeRF: Water-ray matching neural radiance fields for underwater scene reconstruction

Abstract

Neural Radiance Field (NeRF) technology demonstrates immense potential in novel viewpoint synthesis tasks due to its physics-based volumetric rendering process, which is particularly promising in underwater scenes. However, existing underwater NeRF methods face challenges in handling light attenuation caused by the water medium and the lack of real Ground Truth (GT) supervision. To address these issues, we propose WaterHE-NeRF, a novel approach incorporating a water-ray matching field developed based on Retinex theory. This field precisely encodes color, density, and illuminance attenuation in three-dimensional space. WaterHE-NeRF employs an illuminance attenuation mechanism to generate degraded and clear multi-view images, optimizing image restoration by combining reconstruction loss with Wasserstein distance. Furthermore, using histogram equalization (HE) as pseudo-GT, WaterHE-NeRF enhances the network’s accuracy in preserving original details and color distribution. Extensive experiments on real underwater and synthetic datasets validate the effectiveness of WaterHE-NeRF.